Objective Measurement of Audio Quality

ABSTRACT

In an apparatus for objective perceptual evaluation of speech quality, parameters BandwidthRef and BandwidthTest representing the bandwidth are forwarded to a calculator  30  for calculating the relative bandwidth difference ΔBW between a reference signal and a test signal. ΔBW is forwarded to a calculator  32 , which determines the value of a weighting parameter α. Preferably a sealing unit  33  scales or normalizes the disturbance density D and the asymmetric disturbance density DA, for example to the range [0,1]. The values of ΔBW and α are forwarded to a bandwidth compensator  34 , which also receives the preferably scaled disturbance density D and asymmetric disturbance density DA. The bandwidth compensated disturbance densities D*, DA* are forwarded to a linear combiner  42 , which forms a score representing predicted quality of the test signal.

TECHNICAL FIELD

The present invention relates generally to objective measurement ofaudio quality.

BACKGROUND

PEAQ is an ITU-R standard for objective measurement of audio quality,see [1]. This is a method that reads an original and a processed audiowaveform and outputs an estimate of perceived overall quality.

PEAQ performance is limited by its inability to assess the quality ofsignals with large differences in bandwidth. Furthermore, PEAQdemonstrates poor performance when evaluated on unknown data, as it isdependent on neural network weights, trained on the limited database.

PESQ is an ITU-T standard for objective measurement of audio (speech)quality, see [2]. PESQ performance is also limited by its inability toassess the quality of signals with large differences in bandwidth.

SUMMARY

An object of the present invention is to enhance performance forobjective perceptual evaluation of audio quality.

This object is achieved in accordance with the attached patent claims.

Briefly, the present invention involves objective perceptual evaluationof audio quality based on one or several model output variables, andincludes bandwidth compensation of at least one such model outputvariable.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, together with further objects and advantages thereof, maybest be understood by making reference to the following descriptiontaken together with the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating the human hearing and qualityassessment process;

FIG. 2 is a block diagram illustrating speech quality assessment thatmimics the human quality assessment process;

FIG. 3 is a block diagram of an apparatus for performing the originalPEAQ method;

FIG. 4 is a block diagram of an example of a modification in accordancewith the present invention of the apparatus in FIG. 1;

FIG. 5 is a block diagram of a preferred embodiment of a part of anapparatus for objective perceptual evaluation of audio quality inaccordance with the present invention;

FIG. 6 is a flow chart of a preferred embodiment of a part of a methodof objective perceptual evaluation of audio quality in accordance withthe present invention;

FIG. 7 is a block diagram of an embodiment of a part of an apparatus forobjective perceptual evaluation of speech quality in accordance with thepresent invention;

FIG. 8 is a flow chart of an embodiment of a part of a method ofobjective perceptual evaluation of speech quality in accordance with thepresent invention;

FIG. 9 is a block diagram of a preferred embodiment of a part of anapparatus for objective perceptual evaluation of speech quality inaccordance with the present invention; and

FIG. 10 is a flow chart of a preferred embodiment of a part of a methodof objective perceptual evaluation of speech quality in accordance withthe present invention.

DETAILED DESCRIPTION

In the following description elements performing the same or similarfunctions will be denoted by the same reference designations.

The present invention relates generally to psychoacoustic methods thatmimic the auditory perception to assess signal quality. The humanprocess of assessing signal quality can be divided into two main steps,namely auditory processing and cognitive mapping, as illustrated inFIG. 1. An auditory processing block 10 contains the part where theactual sound is being transformed into nerve excitations. This processincludes the Bark scale frequency mapping and the conversion from signalpower to perceived loudness. A cognitive mapping block 12, which isconnected to the auditory processing block 10, is where the brainextracts the most important features of the signal and assesses theoverall quality.

An objective quality assessment procedure contains both a perceptualtrans-form and a cognitive processing to mimic the human perception, asshown in FIG. 2. The perceptual transform 14 mimics the auditoryprocessing and is performed on both the original signal s and thedistorted signal y. The output is a measure of the sound representationsent to the brain. The process includes transforming the signal power toloudness according to a nonlinear, known scale and the transformationfrom Hertz to Bark scale. The ear's sensitivity depends on the frequencyand thresholds of audible sound are calculated. Masking effects are alsotaken into consideration in this step. From this perceptual transform aninternal representation is calculated, which is intended to mimic theinformation sent to the brain. In the cognitive processing block 16features (indicated by {tilde over (s)}_(p) and {tilde over (y)}_(p)respectively) that are expected to describe the signal are selected.Finally the distance d({tilde over (s)}_(p),{tilde over (p)}_(p))between the clean and the distorted signal is calculated in block 18.This distance yields a quality score {circumflex over (Q)}.

PEAQ runs in two modes: 1) Basic and 2) Advanced. For simplicity wediscuss only the Basic version and refer to it as PEAQ, but the conceptsare applicable also to the Advanced version.

As a first step PEAQ transforms the input signal in a perceptual domainby modeling the properties of human auditory systems. Next thealgorithms extracts 11 parameters, called Model Output Variables (MOVs).In the final stage the MOVs are mapped to a single quality grade bymeans of an artificial neural network with one hidden layer. The MOVsare given in Table 1 below. Columns 1 and 2 give their name anddescription, while columns 3 and 4 introduce a notation that will beused in the description of the proposed modification.

TABLE 1 Model Output Notation - Notation - Variable (MOV) DescriptionMOV MOV Group WinModDiff1 Windowed modulation F₁ G₁ differenceAvgModDiff1 Averaged modulation F₂ difference 1 AvgModDiff2 Averagedmodulation F₃ difference 2 TotalNMR Noise-to-mask ratio F₄ G₂RelDistFrames Frequency of audible F₅ distortions MFPD Detectionprobability F₆ G₃ ADB Average distorted block F₇ EHS Harmonic structureof F₈ G₄ the error RmsNoiseLoud Root-mean square of F₉ G₅ the noiseloudness BandwidthRef Bandwidth of the original signal BandwidthTestBandwidth of the processed signal

FIG. 3 is a block diagram of an apparatus for performing the originalPEAQ method. The original and processed (altered) signal are forwardedto respective auditory processing blocks 20, which transform them intorespective internal representations. The internal representations areforwarded to an extraction block 22, which extracts the MOVs, which inturn are forwarded to an artificial neural network 24 that predicts thequality of the processed input signal.

FIG. 4 is a block diagram of an example of a modification in accordancewith the present invention of the apparatus in FIG. 1.

The basic concept of the this embodiment is to replace the neuralnetwork of the original PEAQ (dashed box in FIG. 3) with bandwidthcompensation+quantile-based averaging modules (dashed box in FIG. 4including blocks 26 and 28). The proposed scheme is based on the sameperceptual transform and MOVs extraction as the original PEAQ.

A basic aspect of the present invention is to explicitly account for (inblock 26 in FIG. 4) the fact that with large differences in thebandwidth of the original and processed signal, a majority of the MOVsproduce unreliable results. Thus, according to this aspect the presentinvention compensates for differences in bandwidth between the referencesignal and the test (also called processed) signal.

Another aspect of the present invention is to avoid mapping trained on adatabase (in this case an artificial neural network with 42 parameters).This type of mapping may lead to unreliable results when used with anunknown/new type of data. The proposed mapping (quantile-basedaveraging, block 28 in FIG. 4) has no training parameters.

In the following we will refer to the proposed modification as PEAQ-E(PEAQ Enhanced). PEAQ-E is based on the same MOVs as PEAQ, butpreferably scaled to the range [0,1] (other scaling or normalizingranges are of course also feasible). Instead of feeding a neuralnetwork, as is done in PEAQ, these MOVs are preferably input to atwo-stage procedure that includes bandwidth compensation andquantile-based averaging, see FIG. 4. The bandwidth compensation removesthe main non-linear dependences between MOVs, and allows for use of asimpler mapping scheme (quantile-based averaging instead of a trainedneural network).

The bandwidth compensation transforms each MOV F_(i) into a new MOVF*_(i) (see Table 1 for notation clarification) in accordance with

$\begin{matrix}{{F_{i}^{*} = {{\left( {1 - \alpha} \right)F_{i}} + {{\alpha\Delta}\; {BW}}}}{where}} & (1) \\{{{\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}}{and}} & (2) \\{\alpha = \sqrt{\Delta \; {BW}}} & (3)\end{matrix}$

and where ∥.∥ denotes the absolute value in (2). Here BandwidthRefrepresents a measure of the bandwidth of the original signal andBandwidthTest represents a measure of the bandwidth of the processedsignal.

Although equation (3) gives α as the square root of ΔBW, othercompressing functions of ΔBW are also feasible, for example

α=ΔBW^(0.4)

α=ΔBW^(0.6)  (4)

α=log(ΔBW)

After this bandwidth compensation, the new bandwidth compensated MOVsF*_(i) may be used to train the neural network in PEAQ. However, analternative is to use the quantile based averaging procedure describedbelow.

Quantile-based averaging in accordance with an embodiment of the presentinvention is a multi-step procedure. First the bandwidth compensatedMOVs F*_(i) of the same type are grouped into five groups (see Table 1for group definition), and a characteristic value G₁ . . . G₅ isassigned to each group in accordance with:

$\begin{matrix}{G_{1} = {\frac{1}{3}\left( {F_{1}^{*} + F_{2}^{*} + F_{3}^{*}} \right)}} & (5) \\{G_{2} = {\frac{1}{2}\left( {F_{4}^{*} + F_{5}^{*}} \right)}} & (6) \\{G_{3} = {\frac{1}{2}\left( {F_{6}^{*} + F_{7}^{*}} \right)}} & (7) \\{G_{4} = F_{8}^{*}} & (8) \\{G_{5} = F_{9}^{*}} & (9)\end{matrix}$

These characteristic values represent different aspects of the signals,namely:

-   G₂—a measure of the difference of temporal envelopes of the original    and processed signal.-   G₂—a measure of the ratio of the noise to the masking threshold.-   G₃—a measure of the probability of detecting differences between the    original and processed signal.-   G₄—a measure of the strength of the harmonic structure of the error    signal.-   G₅—a measure of the partial loudness of distortion.

Once the five characteristic values G₁ . . . G₅ have been formed, thesevalues are sorted, and min and max levels are removed, i.e.

{G _(j)}_(j=1) ⁵=sort({G _(k)}_(k=1) ⁵)  (10)

Next the mean of the remaining subset {G_(j)}_(j=2) ⁴ is calculated,which is the output of PEAQ-E, i.e.

$\begin{matrix}{{O\; D\; G} = {\frac{1}{3}\left( {G_{2} + G_{3} + G_{4}} \right)}} & (11)\end{matrix}$

where ODG=Objective Difference Grade.

In equations (5), (6), (7) and (11) the averages may be replaced byweighted averages.

FIG. 5 is a block diagram of a preferred embodiment of a part of anapparatus for objective perceptual evaluation of audio quality inaccordance with the present invention. The parameters BandwidthRef andBandwidthTest are forwarded to a ΔBW calculator 30, and the calculatedrelative bandwidth difference ΔBW is forwarded to an α calculator 32,which determines the value of α in accordance with, for example, one ofthe formulas given in (3) or (4) above. Preferably a scaling unit 33scales or normalizes the model output variables F_(i), for example tothe range [0,1]. The values of ΔBW and α are forwarded to a bandwidthcompensator 34, which also receives the preferably scaled variablesF_(i). In this embodiment the bandwidth compensation is performed inaccordance with (1) above.

Considering the examples given in (3) and (4), it is appreciated that amay be regarded as a function of ΔBW, i.e. α=α(ΔBW). One possibility isto let α be a step function

$\begin{matrix}{\alpha = \left\{ \begin{matrix}{0,{{{if}\mspace{14mu} \Delta \; {BW}} < \Theta}} \\{1,{{{if}\mspace{14mu} \Delta \; {BW}} \geq \Theta}}\end{matrix} \right.} & (12)\end{matrix}$

where Θ is a threshold. In this case (1) reduces to

$\begin{matrix}{F_{i}^{*} = \left\{ \begin{matrix}{F_{i},{{{if}\mspace{14mu} \Delta \; {BW}} < \Theta}} \\{{\Delta \; {BW}},{{{if}\mspace{14mu} \Delta \; {BW}} \geq \Theta}}\end{matrix} \right.} & (13)\end{matrix}$

A further generalization of (1) is given by

F* _(i)=β(ΔBW)F _(i)+α(ΔBW)ΔBW  (14)

where β(ΔBW) is another function of ΔBW.

In general ΔBW is a measure of the distance between BandwidthRef andBandwidthTest. Thus, with a different mapping other measures than (2)are also possible. One example is

ΔBW=(BandwidthRef−BandwidthTest)²  (15)

Returning now to FIG. 5, the bandwidth compensated model outputvariables F*_(i) may be forwarded to the trained artificial network, asin the original PEAQ standard. However, in the preferred embodimentillustrated in FIG. 5, the variables F*_(i) are forwarded to a groupingunit 36, which groups them into different groups and calculates acharacteristic value for each group, as described with reference to(5)-(9) above. These characteristic values G_(k) are forwarded to asorting and selecting unit 38, which sorts them and removes the min andmax values. The remaining characteristic values G₂, G₃, G₄ are forwardedto an averaging unit 40, which forms a measure representing thepredicted quality in accordance with (11)

FIG. 6 is a flow chart of a preferred embodiment of a part of a methodof objective perceptual evaluation of audio quality in accordance withthe present invention. Step S1 determines ΔBW as described above. StepS2 determines α as described above. Step S3 determines the bandwidthcompensated model output variables F*_(i) using the preferably scaledmodel output variables F_(i), as described above. These compensatedvariables may be forwarded to the trained artificial neural network.However, in the preferred embodiment they are instead forwarded to thequantile based averaging procedure, which starts in step S4. Step S4groups the bandwidth compensated model output variables F*_(i) intoseparate model output variable groups. Step S5 forms a set ofcharacteristic values G_(k) (described with reference to (5)-(9)), onefor each group. Step S6 deletes the extreme (Max and MM) characteristicvalues. Finally step S7 forms the predicted quality (ODG) by averagingthe remaining characteristic values.

The present invention has several advantages over the original PEAQ,some of which are:

-   -   PEAQ-E has higher prediction accuracy. Over a set of databases        PEAQ-E has significantly higher correlation with subjective        quality R=0.85, compared to R=0.68 for PEAQ (see Table 2). Even        without quantile based averaging, i.e. with only bandwidth        compensation, R is of the order of 0.80.    -   The preferred embodiment of PEAQ-E with quantile based averaging        is more robust than PEAQ. The worst correlation for a single        database for PEAQ-E is R=0.70, while for PEAQ it is R=0.45 (see        Table 2).    -   The preferred embodiment of PEAQ-E with quantile based averaging        generalizes better for unknown data, as it has no training        parameters, while PEAQ has 42 database trained weights for the        artificial neural network.

Table 2 below gives the correlation coefficient over 14 subjectivedatabases for the original and enhanced PEAQ. All databases are based onMUSHRA methodology, see [3]. As each group corresponds to one type ofdistortion, this operation ignores the contribution of types ofdistortions that are not consistent with the majority.

TABLE 2 R R # (PEAQ) (PEAQ-E) Test description test items 0.6607 0.7339stereo, mixed content, 24 kHz 72 0.7385 0.7038 stereo, mixed content, 48kHz 60 0.924 0.9357 stereo, mixed content, 48 kHz 80 0.6422 0.8447stereo, mixed content, 48 kHz 108 0.4852 0.9238 stereo, mixed content,48 kHz 108 0.5618 0.9192 mono, mixed content, 48 kHz 72 0.9213 0.9284mono, speech, 8 kHz 70 0.9041 0.9225 mono, speech, 8 kHz 70 0.709 0.826mono, speech, 24/32/48 kHz 99 0.6271 0.912 mono, speech, 48 kHz 960.7174 0.7778 mono/stereo, music, 44.1 kHz 239 0.452 0.8381 stereo,speech, 44.1 kHz 90 0.5719 0.9229 stereo, mixed content, 32 kHz 480.6376 0.7352 stereo, mixed content, 16 kHz 72 0.68 0.85

The concept of bandwidth compensation described above may also be usedin other procedures for perceptual evaluation of audio quality. Anexample is the PESQ (Perceptual Evaluation of Speech Quality) standard,see [2]. In this standard the speech quality is predicted from a featurecalled “disturbance density”, which will be denoted D below. Thisfeature is conceptually very close to “RmsNoiseLoud” (F₉ in Table 1) inPEAQ.

The PESQ standard may be summarized as follows. First, in apreprocessing step, the original and processed signals are time andlevel aligned. Next, for both signals, the power spectrum is calculated,on 32 ms frames with 50% overlap. The perceptual transform is performedby mean of conversion to a Bark scale followed by conversion to loudnessdensities. Finally the signed difference between the loudness densitiesof the original and processed signals gives two parameters (model outputvariables), the disturbance density D and asymmetric disturbance densityDA. These two parameters are aggregated over frequency and time toobtain average disturbance densities, which are mapped by means of thesigmoid function to the objective quality.

In PESQ the bandwidth can, for example, be calculated in the followingway (this description follows the procedure in which the bandwidth iscalculated in PEAQ standard):

1. Perform an FFT on the reference signal. Select 1/10 of the frequencybins with largest numbers (that is if your frequency bins are numbered 1to 100, select bins with numbers 91, 92, 93, . . . , 100). Define athreshold level T as the max energy in the selected group of frequencybins. When searching backwards (from high to low frequency bin numbers,in our example from 90, 89 to 1), define BandwidthRef as the firstfrequency bin that has an energy that exceeds the threshold level T by10 dB.2. For the test signal use the threshold level, as calculated from thereference signal (that is, use the same T). Again in the FFT domaindefine BandwidthTest as the frequency bin that has an energy thatexceeds the threshold level T by 10 dB.

To summarize: BandwidthRef and BandwidthTest are just FFT bin numbers ofthe bins that have an energy that exceeds a certain threshold. Thisthreshold is calculated as the max energy among the FFT bins withhighest numbers. After determining BandwidthRef and BandwidthTest thebandwidth compensation of the (preferably scaled) disturbance density Dmay be performed in the same way as discussed in connection withequations (1)-(3) above. This gives

$\begin{matrix}{{D^{*} = {{\left( {1 - \alpha} \right)D} + {{\alpha\Delta}\; {BW}}}}{where}} & (16) \\{{{\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}}{and}} & (17) \\{\alpha = \sqrt{\Delta \; {BW}}} & (18)\end{matrix}$

and where ∥.∥ denotes the absolute value in (17). Other compressingfunctions of ΔBW are also feasible for α, see the discussion for PEAQabove.

The corresponding bandwidth compensation for the (preferably scaled)asymmetric disturbance density DA is

DA*=(1−α)DA+αΔBW  (19)

Considering the examples given in (3) and (4) (or (18)), it isappreciated that α may be regarded as a function of ΔBW, i.e. α=α(ΔBW).One possibility is to let α be a step function

$\begin{matrix}{\alpha = \left\{ \begin{matrix}{0,{{{if}\mspace{14mu} \Delta \; {BW}} < \Theta}} \\{1,{{{if}\mspace{14mu} \Delta \; {BW}} \geq \Theta}}\end{matrix} \right.} & (20)\end{matrix}$

where Θ is a threshold. In this case (16) and (19) reduce to

$\begin{matrix}{D = \left\{ \begin{matrix}{D,{{{if}\mspace{14mu} \Delta \; {BW}} < \Theta}} \\{{\Delta \; {BW}},{{{if}\mspace{14mu} \Delta \; {BW}} \geq \Theta}}\end{matrix} \right.} & (21) \\{{DA} = \left\{ \begin{matrix}{{DA},{{{if}\mspace{14mu} \Delta \; {BW}} < \Theta}} \\{{\Delta \; {BW}},{{{if}\mspace{14mu} \Delta \; {BW}} \geq \Theta}}\end{matrix} \right.} & (22)\end{matrix}$

A further generalization of (16) and (19) is given by

D*=β(ΔBW)D+α(ΔBW)ΔBW  (23)

DA*=β(ΔBW)DA+α(ΔBW)ΔBW  (24)

where β(ΔBW) is another function of ΔBW

In general ΔBW is a measure of the distance between BandwidthRef andBandwidthTest. Thus, with a different mapping other measures than (17)are also possible. One example is

ΔBW=(BandwidthRef−BandwidthTest)²  (25)

FIG. 7 is a block diagram of an embodiment of a part of an apparatus forobjective perceptual evaluation of speech quality in accordance with thepresent invention. The parameters BandwidthRef and BandwidthTest areforwarded to ΔBW calculator 30, and the calculated relative bandwidthdifference ΔBW is forwarded to α calculator 32, which determines thevalue of α in accordance with, for example, one of the formulas given in(18) or (4) above. Preferably a scaling unit 33 scales or normalizes thedisturbance density D, for example to the range [0,1]. The values of ΔBWand α are forwarded to a bandwidth compensator 34, which also receivesthe preferably scaled disturbance density D. In this embodiment thebandwidth compensation is performed in accordance with (16) above.

FIG. 8 is a flow chart of an embodiment of a part of a method ofobjective perceptual evaluation of speech quality in accordance with thepresent invention. Step S1 determines ΔBW as described above. Step S2determines α as described above. Step S3 determines the bandwidthcompensated disturbance density D* using the preferably scaleddisturbance density D, as described above.

FIG. 9 is a block diagram of a preferred embodiment of a part of anapparatus for objective perceptual evaluation of speech quality inaccordance with the present invention. The parameters BandwidthRef andBandwidthTest are forwarded to ΔBW calculator 30, and the calculatedrelative bandwidth difference ΔBW is forwarded to a calculator 32, whichdetermines the value of α in accordance with, for example, one of theformulas given in (18) or (4) above. Preferably a scaling unit 33 scalesor normalizes the disturbance density D and the asymmetric disturbancedensity DA, for example to the range [0,1]. The values of ΔBW and α areforwarded to a bandwidth compensator 34, which also receives thepreferably scaled disturbance density D and asymmetric disturbancedensity DA. In this embodiment the bandwidth compensation is performedin accordance with (16) and (19) above. The bandwidth compensateddisturbance densities D*, DA* are forwarded to a linear combiner 42,which forms the PESQ score representing predicted quality.

FIG. 10 is a flow chart of a preferred embodiment of a part of a methodof objective perceptual evaluation of speech quality in accordance withthe present invention. Step S1 determines ΔBW as described above. StepS2 determines α as described above. Step S3 determines the bandwidthcompensated disturbance density D* and asymmetric disturbance densityDA* using the preferably scaled disturbance density D and asymmetricdisturbance density DA, as described above.

The functionality of the various blocks and steps is typicallyimplemented by one or several micro processors or micro/signal processorcombinations and corresponding software.

It will be understood by those skilled in the art that variousmodifications and changes may be made to the present invention withoutdeparture from the scope thereof, which is defined by the appendedclaims.

ABBREVIATIONS PEAQ Perceptual Evaluation of Audio Quality PESQPerceptual Evaluation of Speech Quality

PEAQ-E PEAQ Enhanced (the proposed modification)

MOV Model Output Variable

MUSHRA MUlti Stimulus test with Hidden Reference and Anchor

ODG Objective Difference Grade REFERENCES

-   [1] ITU-R Recommendation BS.1387-1, Method for objective    measurements of perceived audio quality, 2001.-   [2] ITU-T Recommendation P.862, Methods for objective and subjective    assessment of quality, 2001-   [3] ITU-R Recommendation BS.1534, Method for the subjective    assessment of intermediate quality level of coding systems, 2001

1-28. (canceled)
 29. A method of objective perceptual evaluation ofaudio quality based on at least one model output variable, said methodcomprising; bandwidth compensating said at least one model outputvariable for differences in bandwidth between an original signal and aprocessed signal by applying a function to said at least one modeloutput variable, said function being a linear combination of said atleast one model output variable and a function of the difference betweena measure of the bandwidth of the original signal and a measure of thebandwidth of the processed signal; and wherein the coefficients of thelinear combination are functions of said difference.
 30. The method ofclaim 29, including the step of bandwidth compensating at least one ofthe model output variables F_(i) of the Perceptual Evaluation of AudioQuality (PEAQ) standard, to obtain the corresponding bandwidthcompensated model output variable F*_(i), and where: F₁=WinModDiff1;F₂=AvgModDiff1; F₃=AvgModDiff2; F₄=TotalNMR; F₅=RelDistFrames; F₆=MFPD;F₇=ADB; F₈=EHS; and F₉=RmsNoiseLoud.
 31. The method of claim 30, whereinall model output variables F₁-F₉ are bandwidth compensated, to obtainbandwidth compensated model output variables denoted as F*₁-F*₉.
 32. Themethod of claim 30, wherein the bandwidth compensation is performed inaccordance with F_(i)^(*) = (1 − α)F_(i) + α Δ BW where${\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}$where ∥.∥ denotes the absolute value, BandwidthRef is the measure of thebandwidth of the original signal, BandwidthTest is the measure of thebandwidth of the processed signal, α is a compressing function of ΔBW,and where F*_(i) denotes the bandwidth compensated version of F_(i) 33.The method of claim 32, wherein α=√{square root over (ΔBW)}.
 34. Themethod of claim 30, wherein the bandwidth compensated model outputvariables F*_(i) are used to train a neural network.
 35. The method ofclaim 30, including the further steps of: grouping predeterminedbandwidth compensated model output variables F*_(i) into separate modeloutput variable groups; forming a set of characteristic values G_(k),one for each of said groups; deleting the maximum and minimumcharacteristic values; and averaging the remaining characteristicvalues.
 36. The method of claim 29, including the step of scaling themodel output variables F_(i) to a predetermined interval.
 37. The methodof claim 36, wherein the model output variables F_(i) are scaled to theinterval [0, 1].
 38. The method of claim 29, including the step ofbandwidth compensating the disturbance density D of the PESQ standard,to obtain the bandwidth compensated disturbance density D*.
 39. Themethod of claim 38, wherein the bandwidth compensation is performed inaccordance with D^(*) = (1 − α)D + αΔ BW where${\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}$where ∥.∥ denotes the absolute value, BandwidthRef is the measure of thebandwidth of the original signal, BandwidthTest is the measure of thebandwidth of the processed signal, and α is a compressing function ofΔBW
 40. The method of claim 29, including the step of bandwidthcompensating the asymmetric disturbance density DA of the PESQ standard,to obtain a bandwidth compensated asymmetric disturbance density DA*.41. The method of claim 40, wherein the bandwidth compensation isperformed in accordance with DA^(*) = (1 − α)DA + α Δ BW where${\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}$where ∥.∥ denotes the absolute value, BandwidthRef is the measure of thebandwidth of the original signal, BamdwidthTest is the measure of thebandwidth of the processed signal, and α is a compressing function ofΔBW
 42. The method of claim 41, wherein α=√{square root over (ΔBW)}. 43.An apparatus for objective perceptual evaluation of audio quality basedon at least one model output variable, said apparatus comprising: one ormore processing circuits configured for bandwidth compensating said atleast one model output variable for differences in bandwidth between anoriginal signal and a processed signal by applying a function to said atleast one model output variable, said function being a linearcombination of said at least one model output variable and a function ofthe difference between a measure of the bandwidth of the original signaland a measure of the bandwidth of the processed signal; and wherein thecoefficients of the linear combination are functions of said difference.44. The apparatus of claim 43, wherein the apparatus is configured tobandwidth compensate at least one of the model output variables F_(i) ofthe PEAQ standard, to obtain the corresponding bandwidth compensatedmodel output variable F*_(i), and where: F₁=WinModDiff1; F₂=AvgModDiff1;F₃=AvgModDiff2; F₄=TotalNMR; F₅=RelDistFrames; F₆=MFPD; F₇=ADB; F₈=EHS;and F₉=RmsNoiseLoud.
 45. The apparatus of claim 44, wherein theapparatus is configured to bandwidth compensate all model outputvariables F₁-F₉, to obtain bandwidth compensated model output variablesF*₁-F*₉.
 46. The apparatus of claim 44, wherein the apparatus isconfigured to bandwidth compensate the model output variables F_(i) inaccordance with F_(i)^(*) = (1 − α)F_(i) + α Δ BW where${\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}$where ∥.∥ denotes the absolute value, BandwidthRef is the measure of thebandwidth of the original signal, BandwidthTest is the measure of thebandwidth of the processed signal, α is a compressing function of ΔBW,and where F*_(i) denotes the bandwidth compensated version of F_(i). 47.The apparatus of claim 46, wherein α=√{square root over (ΔBW)}.
 48. Theapparatus of claim 44, wherein the apparatus is configured to use thebandwidth compensated model output variables F*_(i) to train a neuralnetwork.
 49. The apparatus of claim 44, wherein the one or moreprocessing circuits include: a grouping unit adapted to grouppredetermined bandwidth compensated model output variables F*_(i) intoseparate model output variable groups and to form a set ofcharacteristic values G_(k), one for each of said groups; a sorting andselecting unit adapted to delete the maximum and minimum characteristicvalues; and an averaging unit adapted to average the remainingcharacteristic values.
 50. The apparatus of claim 44, wherein the one ormore processing circuits include a scaling unit adapted to scale themodel output variables F_(i) to a predetermined interval.
 51. Theapparatus of claim 50, wherein said scaling unit is adapted to scale themodel output variables F to the interval [0, 1].
 52. The apparatus ofclaim 43, wherein the apparatus is configured to bandwidth compensatethe disturbance density D of the PESQ standard, to obtain a bandwidthcompensated disturbance density D*.
 53. The apparatus of claim 52,wherein the apparatus is configured for bandwidth compensating thedisturbance density D in accordance with D^(*) = (1 − α)D + αΔ BWwhere${\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}$where ∥.∥ denotes the absolute value, BandwidthRef is the measure of thebandwidth of the original signal, BandwidthTest is the measure of thebandwidth of the processed signal, and α is a compressing function ofΔBW
 54. The apparatus of claim 43, wherein the apparatus is configuredto bandwidth compensate the asymmetric disturbance density DA of thePESQ standard, to obtain the bandwidth compensated asymmetricdisturbance density DA*.
 55. The apparatus of claim 54, wherein theapparatus is configured to bandwidth compensate the asymmetricdisturbance density DA in accordance withDA^(*) = (1 − α)DA + α Δ BW where${\Delta \; {BW}} = \frac{{{BandwidthRef} - {BandwidthTest}}}{BandwidthRef}$where ∥.∥ denotes the absolute value, BandwidthRef is the measure of thebandwidth of the original signal, BandwidthTest is the measure of thebandwidth of the processed signal, and α is a compressing function ofΔBW.
 56. The apparatus of claim 55, wherein Δ=√{square root over (ΔBW)}.